In Fig. REF , we show additional qualitative comparisons on the real-world 3DPW dataset. In each sub-figure we show the input frame, the estimated mask of the object of interest using Mask R-CNN [1]}, the prediction of the best-performing baseline model (PIFuHD), and our prediction. From Fig. REF  we observe that REDO recovers complete and smooth prediction of the real-world human in various poses. In contrast, the baseline method often struggles when humans are in rare poses. This is largely due to the difference in the pre-training dataset where PIFuHD is trained on stand-still humans with no occlusion, whereas REDO is trained on the large-scale synthetic dataset SAIL-VOS 3D which comes with humans in challenging poses and with sever occlusions.
